Python (Google Colab) scripts for machine learning-based bibliometric data extraction and topic modelling
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2025
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| author | Omogbene, Temitope Olorunyomi Gebashe, Fikisiwe Cynthia Lawal, Ibraheem Oduola Amoo, Stephen Oluwaseun Aremu, Adeyemi Oladapo |
| author_facet | Omogbene, Temitope Olorunyomi Gebashe, Fikisiwe Cynthia Lawal, Ibraheem Oduola Amoo, Stephen Oluwaseun Aremu, Adeyemi Oladapo |
| contents | <p>This collection includes <strong>Python notebooks</strong> (optimised for <strong>Google Colab</strong>) implementing machine learning and natural language processing (NLP) methods for advanced and complex bibliometric data analysis. The scripts automate keyword normalisation, thematic clustering, and topic modelling using <em>spaCy</em>, <em>scikit-learn</em>, and <em>NLTK</em>. Outputs include structured data suitable for bibliometric visualisation and network interpretation. The workflow enhances bibliometric insights by integrating semantic analysis and unsupervised learning, supporting studies in phytochemistry, metabolomics, ethnopharmacology, and related knowledge domains.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17385016 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Python (Google Colab) scripts for machine learning-based bibliometric data extraction and topic modelling Omogbene, Temitope Olorunyomi Gebashe, Fikisiwe Cynthia Lawal, Ibraheem Oduola Amoo, Stephen Oluwaseun Aremu, Adeyemi Oladapo scientometrics bibliometrix biblioshiny rstudio scopus web of science dimensions python machine learning <p>This collection includes <strong>Python notebooks</strong> (optimised for <strong>Google Colab</strong>) implementing machine learning and natural language processing (NLP) methods for advanced and complex bibliometric data analysis. The scripts automate keyword normalisation, thematic clustering, and topic modelling using <em>spaCy</em>, <em>scikit-learn</em>, and <em>NLTK</em>. Outputs include structured data suitable for bibliometric visualisation and network interpretation. The workflow enhances bibliometric insights by integrating semantic analysis and unsupervised learning, supporting studies in phytochemistry, metabolomics, ethnopharmacology, and related knowledge domains.</p> |
| title | Python (Google Colab) scripts for machine learning-based bibliometric data extraction and topic modelling |
| topic | scientometrics bibliometrix biblioshiny rstudio scopus web of science dimensions python machine learning |
| url | https://doi.org/10.5281/zenodo.17385016 |